--- base_model: BEE-spoke-data/smol_llama-101M-GQA-python datasets: - BEE-spoke-data/pypi_clean-deduped inference: false language: - en license: apache-2.0 metrics: - accuracy model_creator: BEE-spoke-data model_name: smol_llama-101M-GQA-python pipeline_tag: text-generation quantized_by: afrideva source_model: BEE-spoke-data/smol_llama-101M-GQA tags: - python - codegen - markdown - smol_llama - gguf - ggml - quantized - q2_k - q3_k_m - q4_k_m - q5_k_m - q6_k - q8_0 widget: - example_title: Add Numbers Function text: "def add_numbers(a, b):\n return\n" - example_title: Car Class text: "class Car:\n def __init__(self, make, model):\n self.make = make\n \ self.model = model\n\n def display_car(self):\n" - example_title: Pandas DataFrame text: 'import pandas as pd data = {''Name'': [''Tom'', ''Nick'', ''John''], ''Age'': [20, 21, 19]} df = pd.DataFrame(data).convert_dtypes() # eda ' - example_title: Factorial Function text: "def factorial(n):\n if n == 0:\n return 1\n else:\n" - example_title: Fibonacci Function text: "def fibonacci(n):\n if n <= 0:\n raise ValueError(\"Incorrect input\")\n \ elif n == 1:\n return 0\n elif n == 2:\n return 1\n else:\n" - example_title: Matplotlib Plot text: 'import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 10, 100) # simple plot ' - example_title: Reverse String Function text: "def reverse_string(s:str) -> str:\n return\n" - example_title: Palindrome Function text: "def is_palindrome(word:str) -> bool:\n return\n" - example_title: Bubble Sort Function text: "def bubble_sort(lst: list):\n n = len(lst)\n for i in range(n):\n for j in range(0, n-i-1):\n" - example_title: Binary Search Function text: "def binary_search(arr, low, high, x):\n if high >= low:\n mid = (high + low) // 2\n if arr[mid] == x:\n return mid\n elif arr[mid] > x:\n" --- # BEE-spoke-data/smol_llama-101M-GQA-python-GGUF Quantized GGUF model files for [smol_llama-101M-GQA-python](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA-python) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data) | Name | Quant method | Size | | ---- | ---- | ---- | | [smol_llama-101m-gqa-python.fp16.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.fp16.gguf) | fp16 | 203.28 MB | | [smol_llama-101m-gqa-python.q2_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q2_k.gguf) | q2_k | 50.93 MB | | [smol_llama-101m-gqa-python.q3_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q3_k_m.gguf) | q3_k_m | 57.06 MB | | [smol_llama-101m-gqa-python.q4_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q4_k_m.gguf) | q4_k_m | 65.41 MB | | [smol_llama-101m-gqa-python.q5_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q5_k_m.gguf) | q5_k_m | 74.34 MB | | [smol_llama-101m-gqa-python.q6_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q6_k.gguf) | q6_k | 83.83 MB | | [smol_llama-101m-gqa-python.q8_0.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q8_0.gguf) | q8_0 | 108.35 MB | ## Original Model Card: # smol_llama-101M-GQA: python Open In Colab > 400MB of buzz: pure Python programming nectar! 🍯 This model is the general pre-trained checkpoint `BEE-spoke-data/smol_llama-101M-GQA` trained on a deduped version of `pypi` for +1 epoch. Play with the model in [this demo space](https://huggingface.co/spaces/BEE-spoke-data/beecoder-playground). - Its architecture is the same as the base, with some new Python-related tokens added to vocab prior to training. - It can generate basic Python code and markdown in README style, but will struggle with harder planning/reasoning tasks - This is an experiment to test the abilities of smol-sized models in code generation; meaning **both** its capabilities and limitations Use with care & understand that there may be some bugs 🐛 still to be worked out. ## Usage 📌 Be sure to note: 1. The model uses the "slow" llama2 tokenizer. Set use_fast=False when loading the tokenizer. 2. Use transformers library version 4.33.3 due to a known issue in version 4.34.1 (_at time of writing_) > Which llama2 tokenizer the API widget uses is an age-old mystery, and may cause minor whitespace issues (widget only). To install the necessary packages and load the model: ```python # Install necessary packages # pip install transformers==4.33.3 accelerate sentencepiece from transformers import AutoTokenizer, AutoModelForCausalLM # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained( "BEE-spoke-data/smol_llama-101M-GQA-python", use_fast=False, ) model = AutoModelForCausalLM.from_pretrained( "BEE-spoke-data/smol_llama-101M-GQA-python", device_map="auto", ) # The model can now be used as any other decoder ``` ### longer code-gen example Below is a quick script that can be used as a reference/starting point for writing your own, better one :)
🔥 Unleash the Power of Code Generation! Click to Reveal the Magic! 🔮 Are you ready to witness the incredible possibilities of code generation? 🚀. Brace yourself for an exceptional journey into the world of artificial intelligence and programming. Observe a script that will change the way you create and finalize code. This script provides entry to a planet where machines can write code with remarkable precision and imagination. ```python """ simple script for testing model(s) designed to generate/complete code See details/args with the below. python textgen_inference_code.py --help """ import logging import random import time from pathlib import Path import fire import torch from transformers import AutoModelForCausalLM, AutoTokenizer logging.basicConfig(format="%(levelname)s - %(message)s", level=logging.INFO) class Timer: """ Basic timer utility. """ def __enter__(self): self.start_time = time.perf_counter() return self def __exit__(self, exc_type, exc_value, traceback): self.end_time = time.perf_counter() self.elapsed_time = self.end_time - self.start_time logging.info(f"Elapsed time: {self.elapsed_time:.4f} seconds") def load_model(model_name, use_fast=False): """ util for loading model and tokenizer""" logging.info(f"Loading model: {model_name}") tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=use_fast) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) model = torch.compile(model) return tokenizer, model def run_inference(prompt, model, tokenizer, max_new_tokens: int = 256): """ run_inference Args: prompt (TYPE): Description model (TYPE): Description tokenizer (TYPE): Description max_new_tokens (int, optional): Description Returns: TYPE: Description """ logging.info(f"Running inference with max_new_tokens={max_new_tokens} ...") with Timer() as timer: inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=max_new_tokens, min_new_tokens=8, renormalize_logits=True, no_repeat_ngram_size=8, repetition_penalty=1.04, num_beams=4, early_stopping=True, ) text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] logging.info(f"Output text:\n\n{text}") return text def main( model_name="BEE-spoke-data/smol_llama-101M-GQA-python", prompt:str=None, use_fast=False, n_tokens: int = 256, ): """Summary Args: model_name (str, optional): Description prompt (None, optional): specify the prompt directly (default: random choice from list) n_tokens (int, optional): max new tokens to generate """ logging.info(f"Inference with:\t{model_name}, max_new_tokens:{n_tokens}") if prompt is None: prompt_list = [ ''' def print_primes(n: int): """ Print all primes between 1 and n """''', "def quantum_analysis(", "def sanitize_filenames(target_dir:str, recursive:False, extension", ] prompt = random.SystemRandom().choice(prompt_list) logging.info(f"Using prompt:\t{prompt}") tokenizer, model = load_model(model_name, use_fast=use_fast) run_inference(prompt, model, tokenizer, n_tokens) if __name__ == "__main__": fire.Fire(main) ``` Wowoweewa!! It can create some file cleaning utilities.
---